Tea-tasting, a Python package for the statistical analysis of A/B tests
I'm excited to introduce tea-tasting, a Python package for the statistical analysis of A/B tests It features Student's t-test, Bootstrap, variance reduction using CUPED, power analysis, and other statistical methods. tea-tasting supports a wide range of data backends, including BigQuery, ClickHouse, PostgreSQL, Snowflake, Spark, and more, all thanks to Ibis. I consider it ready for important tasks and use it for the analysis of switchback experiments in my work.
In plain words
Tea-tasting is a Python package for statistical analysis of A/B tests that implements Student's t-test, Bootstrap, CUPED variance reduction, and power analysis methods. It connects to multiple data backends including BigQuery, ClickHouse, PostgreSQL, Snowflake, and Spark through Ibis. Designed for data analysts and researchers conducting controlled experiments, the package is built for production use and handles switchback experiment analysis.
written from the facts on this page · September 2026
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